Interested in running a Hadoop proof of concept on enterprise-class storage? Download this solutions guide to get a technical overview on building Hadoop on NetApp E-series storage. NetApp Open Solution for Hadoop delivers big analytics with preengineered, compatible, and supported solutions based on high-quality storage platforms so you reduce the cost, schedule, and risk of do-it-yourself systems and relieving the skills gap most organizations have with Hadoop. See how on going operational and maintenance costs can be reduced with a high available and scalable Hadoop solution.

Read how the NetApp Distributed Content Repository Solution is an efficient and risk-reducing active archive solution. Based on customer data, Forrester created a composite organization and concluded that the NetApp Distributed Content Repository delivered a three year ROI of 47% with a payback period of 1.3 months. The key benefits are reduced risk of losing unregulated archived data, denser storage, storage solution efficiency, and compliance for regulated data. The study also provides readers with a framework to do their own financial impact evaluation.
Source: The Total Economic Impact Of The NetApp Distributed Content Repository Solution (StorageGRID On E-Series), a commissioned study conducted by Forrester Consulting on behalf of NetApp, March 2013.

Advanced analytics strategies yield the greatest benefits in terms of improving patient and business outcomes when applied across the entire healthcare ecosystem. But the challenge of collaborating across organizational boundaries in order to share information and insights is daunting to many stakeholders.
In this worldwide survey of 555 healthcare providers, payers and life sciences organizations, you will learn the importance of implementing collaborative analytics strategies that:
Manage, integrate and interpret data generated at all stages of the healthcare value chain
Achieve the right balance of skills in order to translate data into actionable insights
Focus on executive sponsorship and enterprise-wide adoption with metrics to measure and track success
Position yourself to harness data, create and share insights, make informed decisions, and improve the performance of the entire healthcare ecosystem in which you operate.

Life Sciences organizations need to be able to build IT infrastructures that are dynamic, scalable, easy to deploy and manage, with simplified provisioning, high performance, high utilization and able to exploit both data intensive and server intensive workloads, including Hadop MapReduce. Solutions must scale, both in terms of processing and storage, in order to better serve the institution long-term. There is a life cycle management of data, and making it useable for mainstream analyses and applications is an important aspect in system design. This presentation will describe IT requirements and how Technical Computing solutions from IBM and Platform Computing will address these challenges and deliver greater ROI and accelerated time to results for Life Sciences.

Whether in high-performance computing, Big Data or analytics, information technology has become an essential tool in today’s hyper-competitive business landscape. Organizations are increasingly being challenged to do more with less and this is fundamentally impacting the way that IT infrastructure is deployed and managed. In this short e-book, learn the top ten ways that IBM Platform Computing customers are using technologies like IBM Platform LSF and IBM Platform Symphony to help obtain results faster, share resources more efficiently, and improve the overall cost-effectiveness of their global IT infrastructure.

IBM Platform Computing Cloud Service lets users economically add computing capacity by accessing ready-to-use clusters in the cloud-delivering high performance that compares favorably to cloud offerings from other providers. Tests show that the IBM service delivers the best (or ties for the best) absolute performance in all test categories. Learn More.

In today’s stringent financial services regulatory environment with exponential growth of data and dynamic business requirements, Risk Analytics has become integral to businesses. IBM Algorithmics provides very sophisticated analyses for a wide range of economic scenarios that better quantify risk for multiple departments within a firm, or across the enterprise. With Algorithmics, firms have a better handle on their financial exposure and credit risks before they finalize real-time transactions. But this requires the performance and agility of a scalable infrastructure; driving up IT risk and complexity. The IBM Application Ready Solution for Algorithmics provides an agile, reliable and high-performance infrastructure to deliver trusted risk insights for sustained growth and profitability. This integrated offering with a validated reference architecture delivers the right risk insights at the right time while lowering the total cost of ownership.

Whether in high-performance computing, Big Data or analytics, information technology has become an essential tool in today’s hyper-competitive business landscape. Organizations are increasingly being challenged to do more with less and this is fundamentally impacting the way that IT infrastructure is deployed and managed. In this short e-book, learn the top ten ways that IBM Platform Computing customers are using technologies like IBM Platform LSF and IBM Platform Symphony to help obtain results faster, share resources more efficiently, and improve the overall cost-effectiveness of their global IT infrastructure.

In this white paper, we look at various cloud models, and assess their suitability to solve IT challenges. We provide recommendations on what to look for in a cloud provider. Finally, we take a look at the IBM Cloud portfolio.

Every day, the world creates 2.5 quintillion bytes of data and businesses are realizing tangible results from investments in big data analytics. IBM Spectrum Scale (GPFS) offers an enterprise class alternative to Hadoop Distributed File System (HDFS) for building big data platforms and provides a range of enterprise-class data management features. Spectrum Scale can be deployed independently or with IBM’s big data platform, consisting of IBM InfoSphere® BigInsights™ and IBM Platform™ Symphony. This document describes best practices for deploying Spectrum Scale in such environments to help ensure optimal performance and reliability.

According to our global study of more than 800 cloud decision makers and users are becoming increasingly focused on the business value cloud provides. Cloud is integral to mobile, social and analytics initiatives – and the big data management challenge that often comes with them and it helps power the entire suite of game-changing technologies. Enterprises can aim higher when these deployments are riding on the cloud. Mobile, analytics and social implementations can be bigger, bolder and drive greater impact when backed by scalable infrastructure. In addition to scale, cloud can provide integration, gluing the individual technologies into more cohesive solutions. Learn how companies are gaining a competitive advanatge with cloud computing.

There is a lot of hype around the potential of big data and organizations are hoping to achieve new innovations in products and services with big data and analytics driving more concrete insights about their customers and their own business operations. To meet these challenges, IBM has introduced IBM® Spectrum Scale™. The new IBM Spectrum Scale storage platform has grown from GPFS, which entered the market in 1998. Clearly, IBM has put significant development into developing this mature platform.
Spectrum Scale addresses the key requirements of big data storage - extreme scalability for growth, reduced overhead of data movement, easy accessibility , geographic location independence and advanced storage functionality. Read the paper to learn more!

The IBM Spectrum Scale solution provided for up to 11x better throughput results than EMC Isilon for Spectrum Protect (TSM) workloads. Using published data, Edison compared a solution comprised of EMC® Isilon® against an IBM® Spectrum Scale™ solution. (IBM Spectrum Scale was formerly IBM® General Parallel File System™ or IBM® GPFS™, also known as code name Elastic Storage). For both solutions, IBM® Spectrum Protect™ (formerly IBM Tivoli® Storage Manager or IBM® TSM®) is used as a common workload performing the backups to target storage systems evaluated.

6 criteria for evaluating a high-performance cloud services providers
Engineering, scientific, analytics, big data and research workloads place extraordinary demands on technical and high-performance computing (HPC) infrastructure. Supporting these workloads can be especially challenging for organizations that have unpredictable spikes in resource demand, or need access to additional compute or storage resources for a project or to support a growing business. Software Defined Infrastructure (SDI) enables organizations to deliver HPC services in the most efficient way possible, optimizing resource utilization to accelerate time to results and reduce costs. SDI is the foundation for a fully integrated environment, optimizing compute, storage and networking infrastructure to quickly adapt to changing business requirements, and dynamically managing workloads and data, transforming a s

Building applications for handling big data requires laser-like focus on solutions that allow you to deliver scalable, reliable and flexible infrastructure for fast-growing analytics environments. This paper provides 6 best practices for selecting the “right” infrastructure—one that is optimized for performance, flexibility and long-term value.

Hadoop: Moving Beyond the Big Data Hype
Let’s face it. There is a lot of hype surrounding Big Data and adoop, the defacto Big Data technology platform. Companies want to mine and act on massive data sets, or Big Data, to unlock insights that can help them improve operational efficiency, delight customers, and leapfrog their competition.
Hadoop has become popular to store massive data sets because it can distribute them across inexpensive commodity servers. Hadoop is fundamentally a file system (HDFS or Hadoop Distributed File System) with a specialized programming model (MapReduce) to process the
data in the files. Big Data has not lived up to expectations so far, partly because of limitations of Hadoop as a technology.

As one of the most exciting and widely adopted
open-source projects, Apache Spark in-memory
clusters are driving new opportunities for application
development as well as increased intake of
IT infrastructure. Apache Spark is now the most
active Apache project, with more than 600 contributions
being made in the last 12 months by more
than 200 organizations. A new survey conducted by
Databricks—of 1,417 IT professionals working with
Apache Spark finds that high-performance analytics
applications that can work with big data are driving
a large proportion of that demand. Apache Spark is
now being used to aggregate multiple types of data
in-memory versus only pulling data from Hadoop.
For solution providers, the Apache Spark technology
stack is a significant player because it’s one
of the core technologies used to modernize data
warehouses, a huge segment of the IT industry that
accounts for multiple billions in revenue.
Spark holds much promise for the future—with
data lakes—a storage repo

Read this new IDC Report about how today's enterprise datacenters are dealing with new challenges that are far more demanding than ever before. Foremost is the exponential growth of data, most of it unstructured data. Big data and analytics implementations are also quickly becoming a strategic priority in many enterprises, demanding online access to more data, which is retained for longer periods of time. Legacy storage solutions with fixed design characteristics and a cost structure that doesn't scale are proving to be ill-suited for these new needs. This Technology Spotlight examines the issues that are driving organizations to replace older archive and backup-and-restore systems with business continuity and always-available solutions that can scale to handle extreme data growth while leveraging a cloudbased pricing model. The report also looks at the role of Storiant and its long-term storage services solution in the strategically important long-term storage market.

Emerging storage vendors offer data center managers and storage administrators new antidotes for their storage challenges. This research details five companies that provide innovative storage capabilities via new architecture and deployment methods, and looks back at two past Cool Vendors.

This white paper explores strategies to leverage the steady flow of new, advanced real-time streaming data analytics (RTSA) application development technologies. It defines a thoughtful approach to capitalize on the window of opportunity to benefit
from the power of real-time decision making now, and still be able to move to new and emerging technologies as they become enterprise ready.